Method for optimizing net present value of a cross-selling marketing campaign

• US 8,285,577 B1
• Filed: 07/26/2011
• Issued: 10/09/2012
• Est. Priority Date: 08/06/1999
• Status: Expired due to Term

• Current Assignee: Experian Information Solutions Incorporated (Experian plc)
• Original Assignee: Experian Information Solutions Incorporated (Experian plc)
• Inventors: Galperin, Yuri, Fishman, Vladimir, Gibiansky, Leonid
• Primary Examiner(s): Sterrett, Jonathan G
• Application Number:
  US13/190,832
• Time in Patent Office: 441 Days
• Field of Search: 705/7.12, 705/35
• US Class Current: 705/7.12
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Claims

1. A method comprising:

at least one computer processor formulating a linear optimization problem with a plurality of variables and that takes into account at least;

a plurality of customer constraints stored in computer storage and comprising at least one of an eligibility condition constraint, a peer group logic constraint, and a maximum number of offers constraint; and

a plurality of economic, business, or consumer constraints stored in computer storage, wherein each constraint is reflective of an economic goal of a business to consumer decisioning strategy;

the computer processor reducing the linear optimization problem to a non-linear problem with a feasible number of dimensions, wherein the non-linear problem is mathematically equivalent to the linear optimization problem; and

the computer processor selecting a business to consumer decisioning strategy with desired expected utility and that satisfies the constraints at least in part by iteratively solving the non-linear problem on a sample of customers within a pre-defined tolerance, wherein the selected consumer decisioning strategy identifies specific customers that are to receive specific decision options, and wherein the non-linear problem takes into account at least;

a plurality of behavioral probabilities that each represent a probability that a specific customer will respond to a specific decision option; and

a plurality of profitabilities that each represent a profitability resulting from a specific customer responding to the specific decision option.

2. The method of claim 1, wherein selecting a strategy with desired expected utility comprises selecting a strategy that is expected to have a higher value than another business to consumer decisioning strategy.

3. The method of claim 1, wherein selecting a strategy with desired expected utility comprises selecting a utility function and using the utility function to calculate an expected utility of the strategy.

4. The method of claim 1, wherein the customer constraints are expressed as linear functions.

5. The method of claim 4, wherein the economic and business constraints are expressed as linear functions.

6. The method of claim 1, wherein the customer constraints are expressed as at least one inequality.

7. The method of claim 6, wherein the economic and business constraints are expressed as at least one inequality.

8. The method of claim 1, wherein selecting a strategy with desired expected utility further comprises generating a plurality of solicitation values that define which customers receive which decision options.

9. A decisioning strategy optimization system comprising:

a computer processor configured to execute software components;

at least one data repository of computer storage containing at least one collection of data comprising;

customer data related to a plurality of customers;

behavioral probability data representing a plurality of probabilities that a specific customer will respond to a specific decision option;

profitability data representing, for each of a plurality of customers, value resulting from a specific customer responding to a specific decision option;

customer constraint data representing a plurality of customer constraints, wherein the customer constraints comprise at least one of an eligibility condition constraint, a peer group logic constraint, and a maximum number of offers constraint; and

economic constraint data representing a plurality of economic constraints, wherein each economic constraint is reflective of an economic goal of a decisioning strategy;

a first software component configured to cause the computer processor to formulate a linear optimization problem with a plurality of variables;

a second software component configured to cause the computer processor to reduce the linear optimization problem to a substantially equivalent non-linear problem with a feasible number of dimensions; and

a third software component configured to cause the computer processor to select a decisioning strategy with desired expected utility and that satisfies stored customer and economic constraints at least in part by iteratively solving the non-linear problem on a sample of customers within a pre-defined tolerance, wherein the non-linear problem takes into account at least some of the stored behavioral probabilities and stored profitabilities and the decisioning strategy identifies specific customers that are to receive specific decision options.

10. The system of claim 9, wherein the at least one data repository comprises at least one database.

11. The system of claim 9, wherein the third software component is configured to select a decisioning strategy with desired expected utility by selecting a decisioning strategy that is expected to have a business value that is higher than another decisioning strategy.

12. The system of claim 9, wherein the second software component selects a decisioning strategy with desired expected utility by selecting a utility function and using the utility function to calculate an expected utility of the decisioning strategy.

13. The system of claim 9, wherein the customer constraints are expressed as linear functions.

14. The system of claim 12, wherein the economic constraints are expressed as linear functions.

15. The system of claim 9, wherein the customer constraints are expressed as at least one inequality.

16. The system of claim 15, wherein the economic constraints are expressed as at least one inequality.

17. The system of claim 9, wherein the second software module is further configured to generate and store in the at least one data repository a plurality of solicitation values that define which customers receive which decision options.